A comparison of the different multiple response optimization techniques for turning operation of AISI O1 tool steel

Ravinder Kataria, Jatinder Kumar


In this article, the effect of several process parameters such as tool nose radius, speed,feed and depth of cut on the machining performance of turning operation has beenstudied using AISI O1 tool steel as a work material. The machining characteristicsthat are being studied are material removal rate (MRR) and surface roughness (SR)of machined surface. Taguchi method is utilized for single response optimization. Formulti-response optimization, weighted signal-to-noise ratio (WSN), grey relationalanalysis (GRA), utility concept and technique for order preference by similarity toideal solution (TOPSIS) method have been utilized and their performance is evaluated.WSN method has been found to produce best results for multi-response optimizationfor this study.


Material removal rate; multi-response optimization; surface roughness; Taguchi method; weighted signal-to-noise ratio.

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Aggarwal, A., & Singh, H. 2008. Optimization of multiple quality characteristic for CNC turning under

cryogenic cutting environment using desirability function. Journal of Materials Processing and

Technology 205:42-50.

Antony, J. 2000. Multi-response optimization in industrial experiments using Taguchi’s quality loss

function and principal component analysis. Quality Reliability Engineering International 16:3-8.

Bhattacharya, A., Das, S., Majumder, P., & Batish, A. 2009. Estimating the effect of cutting parameters

on surface finish and power consumption during high speed machining of AISI 1045 steel using

Taguchi design and ANOVA. Production Engineering research Development 3:31-40.

Castillo, E.D., Montgomery, D.C., & McCarville, D. 1996. Modified desirability functions for multiple

response optimizations. Journal of Quality Technology 28:337–345.

Chi, B.C., Cheng, C.J., Lee, & E.S. 2002. Neuro-fuzzy and genetic algorithm in multiple response

optimizations. An International Journal of Computers & Mathematics with Applications


Chiang, T.L., & Su, C.T. 2003. Optimization of TQFP modeling process using neuro-fuzzy-GA

approach. Europen Journal of Operation Research 147:156–164.

Deng, J. 1982. Control problem of grey system. System and Control letter 1 (15) 288-294.

Derringer, G., & Suich R. 1980. Simultaneous optimization of several response variables. Journal of

Quality Technology 12:214–219.

Dhabale, R.,Jatti, V.S., & Singh, T.P. 2014. Optimization of Surface Roughness of AlMg1SiCu in

Turning Operation Using Genetic Algorithm. Applied Mechanics and Materials, 647:592-594.

Feng, C. X., & Wang, X. 2002. Development of Empirical Models for Surface Roughness Prediction in

Finish Turning. International Journal of Advanced Manufacturing Technology 20:348-356.

Gauri, S.K., & Chakraborty, S. 2008. Optimization of multi-response for WEDM process using

weighted principal components. International Journal of Advanced Manufacturing Technology


Haq, A.N., Marimuthu, P., & Jeyapaul, R. 2007. Multi Response Optimization of machining parameters

of drilling AI/SiC metal matrix composite using grey relation analysis in the Taguchi method.

International Journal of Advanced Manufacturing Technology 37:250-255.

Harrington, J. 1965. The Desirability function. Industrial Quality Control 21(10):494-498.

Homami, R. M., Tehrani, A.F., Mirzadeh, H., Movahedi, B., & Azimifar, F. 2014. Optimization of turning process using artificial intelligence technology. International Journal of Advanced

Manufacturing Technology 70:1205–1217.

Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of

Educational Psychology 24:417-441.

Hsieh, K.L., & Tong, L.I. 2001. Optimization of multiple quality responses involving qualitative and

quantitative characteristics in IC manufacturing using neural networks. Computers In Industry


Jeyapaul, R., Shahabudeen, P., & Krishnaiah, K. 2005. Simultaneous optimization of multi-response

problems in the Taguchi method using genetic algorithm. International Journal of Advanced

Manufacturing Technology 30:870-878.

Kaladhar, M., Subbaiah, K.V., Rao, C.K., & Rao, K.N. 2011. Application of Taguchi approach and

Utility Concept in solving the multi-objective problem when turning AISI 202 Austenitic Stainless

Steel. Journal of Engineering Science and Technology Review 4(1):55-61.

Kaladhar, M., Subbaiah, K.V., Rao, Ch.S., & Rao, K.N. 2010. Optimization of process parameters in

turning of AISI202 Austenitic Stainless Steel. ARPN Journal of engineering and applied sciences


Khuri, A.I., & Conlon, M. 1981. Simultaneous optimization of multiple responses represented by

polynomial regression functions. Technometrics 23:363–375.

Kim, K., & Lin, D. 2000. Simultaneous optimization of multiple responses by maximizing exponential

desirability functions. Journal of the Royal Statistical Society Series C Applied Statistic


Kumar, J., & Khamba, J.S. 2010. Multi-response optimization in ultrasonic machining of titanium

using Taguchi’s approach and utility concept. International journal of Manufacturing Research.


Kumar, P., Barua, P.B., & Gaindhar, J.L. 2000. Quality optimization (multi-characteristics) through

Taguchi technique and utility concept. Quality and Reliability Engineering International 16: 475-

Kumar, Y. & Singh, H. 2014. Multi-response optimization in dry turning process using Taguchi’s approach

and utility concept. Procedia Materials Science 5: 2142-2151.

Liao, H.C 2006. Multi-response optimization using weighted principal component. International Journal

of Advanced Manufacturing Technology 27:720–725.

Mahapatra, S.S., & Patnaik, A. 2007. Optimization of wire electrical discharge machining (WEDM)

process parameters using Taguchi method. International Journal of Advanced Manufacturing

Technology 34:911–925.

Montgomery, D.C. 2001. Design and analysis of experiments. Wiley, Singapore. Pp. 219-221.

Neselia, S., & Yaldiz, S. 2011. Optimization of tool geometry parameters for turning operation based on

the response surface methodology. Measurement, 44:580-587.

Ozel, T., Hsu, T., & Erol, Z. 2005. Effects of cutting edge geometry, work piece hardness, feed rate and

cutting speed on surface roughness and force in the finishing turning of hardened of AISI H13 steel.

International Journal of Advance Manufacturing Technology 25:262-269.

Pan, L.K., Wang, C.C., Wei, S.L., & Sher, H.F. 2007. Optimizing multiple quality characteristics via

Taguchi method-based Grey analysis. Journal of Material Process Technology 182:107–116.

Pasandideh, S.H.R., & Niaki, S.T.A. 2006. Multi-response simulation optimization using genetic algorithm

within desirability function framework. Applied Mathematics and Computation 175(1):366–382.

Pearson, K. 1901. On line and planes of closet fit to system of point in space. Philosophical magazine


Phadke, M.S. 1989. Quality engineering using robust design. Prentice-Hall, England Cliffs Pp.108-112.

Ramakrishnan, R., & Karunamoorthy, L. 2006. Multi response optimization of wire EDM operations

using robust design of experiments. International Journal of Advanced Manufacturing Technology


Roos, J. 1996. Taguchi techniques for quality engineering, McGraw-Hill, Singapore. Pp.63-75.

Singh, H. 2008. Optimizing the Tool life of Carbide Inserts for Turned parts using Taguchi’s Design

of Experiment Approach. Proceedings of International Multi Conference of Engineering’s and

Computer Scientists, Hong Kong.

Singh, H., & Kumar, P. 2006, Optimizing feed force for turned parts through the Taguchi technique.

Sadhana 31(6):671-681.

Singh, P.N., Raghukandan, K., & Pai, B.C. 2004. Optimization by grey relation analysis of EDM

parameters on machining AI-10%Sic Composites. Journal of material Processing Technology 55-


Su, C.T., & Tong, L.I. 1997. Multi-response robust design by principal component analysis. Total Quality

Management 8:409-416.

Tong, L.I., & Su, C.T. 1997. Optimizing multi-response problems in Taguchi method by Fuzzy multiple

attribute decision making. Quality and reliability engineering International 13(1):25-34.

Tong, L.I., & Wang, C.H. 2002. Multi-response optimization using principal component analysis and

grey relational analysis. International Journal of Industrial Engineering 9:343–350.

Tong, L.I., Chen, C.C., & Wang, C.H. 2007. Optimization of multi-response processes using the VIKOR

method. International Journal of Advanced Manufacturing Technology 31:1049–1057.

Tong, L.I., Wang, C.H., & Chen, H.C. 2005. Optimization of multiple responses using principal

component analysis and technique for order preference by similarity to ideal solution. International

Journal of Advanced Manufacturing Technology 27:407–414.

Umar, U., Qudeiri, J.A., Hussen, H.A.M., Khan, A.A., & Al-ahmari, A.R. 2014. Multi-objective

optimization of oblique turning operations using finite element model and genetic algorithm.

International Journal of Advanced Manufacturing Technology 71:593–603.

Walia, R.S., Shan, H.S., & Kumar, P. 2006. Multi-response optimization of CAFAAFM process through

taguchi method and utility concept. Materials and Manufacturing Processes 21:907-914.

Yang, W.H., & Tarng, Y.S 1998. Design optimization of cutting parameters for turning operations based

on the taguchi method. Journal of Materials processing Technology 84:122-129.

Yoon, K. & Hwang, C.L. 1995. Multiple Attribute Decision Making: An Introduction. Thousand Oaks,

CA:Sage. Pp 5-7.


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